Search published articles

Showing 8 results for Taguchi Method

Mangesh Phate, Shraddha Toney, Vikas Phate,
Volume 0, Issue 0 (3-2020)

In the present work, a model based on dimensional analysis (DA) coupled with the Taguchi method to analyze the impact of silicon carbide (SiC) has been presented. The wire cut electrical discharge machining (WEDM) performance of aluminium silicon carbide (AlSiC) metal matrix composite (MMC) has been critically examined. To formulate the DA based models, total 18 experiments were conducted using Taguchi’s L18 mixed plan of experimentation. The input data used in the DA models are a pulse on time, pulse off time, wire feed rate, % SiC, wire tension, flushing pressure etc. According to these process parameters, DA models for the surface roughness and the material removal rate was predicted. The formulated DA models have shown a strong correlation with the experimental data. The analysis of variance (ANOVA) has been used to find out the impact of individual parameters on response parameters.  
Rashed Sahraeian,
Volume 25, Issue 1 (2-2014)

In this paper the problem of serial batch scheduling in a two-stage hybrid flow shop environment with minimizing Makesapn is studied. In serial batching it is assumed that jobs in a batch are processed serially, and their completion time is defined to be equal to the finishing time of the last job in the batch. The analysis and implementation of the prohibited transference of jobs among the machines of stage one in serial batch is the main contribution of this study. Machine set-up and ready time for all jobs are assumed to be zero and no Preemption is allowed. Machines may not breakdown but at times they may be idle. As the problem is NP-hard, a genetic algorithm is developed to give near optimal solutions. Since this problem has not been studied previously, therefore, a lower bound is developed for evaluating the performance of the proposed GA. Many test problems have been solved using GA and results compared with lower bound. Results showed GA can obtain a near optimal solution for small, median and large size problems in reasonable time.
Hadi Mokhtari , Ashkan Mozdgir,
Volume 26, Issue 2 (7-2015)

Assembly lines are special kinds of production systems which are of great importance in the industrial production of high quantity commodities. In many practical manufacturing systems, configuration of assembly lines is fixed and designing a new line may be incurred huge amount of costs and thereby it is not desirable for practitioners. When some changes related to market demand occur, it is worthwhile to re-balance an existing line rather than balancing a new one. Hence, in this paper we suggest a re-balancing model of an existing assembly line in which a new demand related cycle time (CT) is embedded to the traditional assembly line balancing problem (ALBP) as a new parameter. It does not focus on balancing a new line instead it considers a more realistic problem which is re-balancing an existing line. The objective is to re-schedule the tasks in order to reduce the current CT to the new required one such that two criteria are optimized: (i) minimization of the incurred costs and (ii) minimization of non-smoothing of reconfigured line. To solve the considered problem, an effective differential evolution algorithm is developed. Furthermore, to enhance the performance of algorithm, its parameters are optimized by the use of Taguchi method which is a conventional statistical technique for parameter design. The obtained results from computational experiments on benchmark instances show the effectiveness of suggested algorithm against other methods.


Sujit Kumar Jha,
Volume 27, Issue 2 (6-2016)

Manufacturing process frequently employs optimization of machining parameters in order to improve product quality as well as to enhance productivity. The material removal rate is a significant indicator of the productivity and cost efficiency of the process. Taguchi method has been implemented for assessing favorable (optimal) machining condition during the machining of nylon by considering three important cutting parameters like cutting speed, feed rate and depth of cut during machining on CNC. The objective of the paper is to find out, which process parameters having more impacts on material removal rate during turning operation on nylon using analysis of variance (ANOVA). An Orthogonal array has been constructed to find the optimal levels of the turning parameters and further signal-to-noise (S/N) ratio has been computed to construct the analysis of variance table. The results of ANOVA shown that feed rate has most significant factor on MRR compare to cutting speed and depth of cut for nylon. The confirmation experiments have conducted to validate the optimal cutting parameters and improvement of MRR from initial conditions is 555.56%.

Parviz Fattahi, Bahman Ismailnezhad,
Volume 27, Issue 2 (6-2016)

In this paper, a stochastic cell formation problem is studied using queuing theory framework and considering reliability. Since cell formation problem is NP-Hard, two algorithms based on genetic and modified particle swarm optimization (MPSO) algorithms are developed to solve the problem. For generating initial solutions in these algorithms, a new heuristic method is developed, which always creates feasible solutions. Moreover, full factorial and Taguchi methods are implemented to set crucial parameters in the solutions procedures. Deterministic method of branch and bound (B&B) algorithm is used to evaluate the results of modified particle swarm optimization algorithm and the genetic algorithm. The results indicate that proposed algorithms have better performance in quality of the metaheurstic algorithms final answer and solving time compared with the method of Lingo software’s B&B algorithm. The solution of two metaheurstic algorithms is compared by t test. Ultimately, the results of numerical examples indicate that considering reliability has significant effect on block structures of machine-part matrixes.

Esmaeil Mehdizadeh, Amir Fatehi-Kivi,
Volume 28, Issue 1 (3-2017)

In this paper, we propose a vibration damping optimization algorithm to solve a fuzzy mathematical model for the single-item capacitated lot-sizing problem. At first, a fuzzy mathematical model for the single-item capacitated lot-sizing problem is presented. The possibility approach is chosen to convert the fuzzy mathematical model to crisp mathematical model. The obtained crisp model is in the form of mixed integer linear programming (MILP) which can be solved by existing solver in crisp environment to find optimal solution. Due to the complexity and NP-hardness of the problem, a vibration damping optimization (VDO) is used to solve the model for large-scale problems.  To verify the performance of the proposed algorithm, we computationally compared the results obtained by the VDO algorithm with the results of the branch-and-bound method and two other well-known meta-heuristic algorithms namely simulated annealing (SA) and genetic algorithm (GA). Additionally, Taguchi method is used to calibrate the parameters of the meta-heuristic algorithms. Computational results on a set of randomly generated instances show that the VDO algorithm compared with the other algorithms can obtain appropriate solutions.

Keyvan Roshan, Mehdi Seifbarghy, Davar Pishva,
Volume 28, Issue 4 (11-2017)

Preventive healthcare aims at reducing the likelihood and severity of potentially life-threatening illnesses by protection and early detection. In this paper, a bi-objective mathematical model is proposed to design a network of preventive healthcare facilities so as to minimize total travel and waiting time as well as establishment and staffing cost. Moreover, each facility acts as M/M/1 queuing system. The number of facilities to be established, the location of each facility, and the level of technology for each facility to be chosen are provided as the main determinants of a healthcare facility network. Since the developed model of the problem is of an NP-hard type, tri-meta-heuristic algorithms are proposed to solve the problem. Initially, Pareto-based meta-heuristic algorithm called multi-objective simulated annealing (MOSA) is proposed in order to solve the problem. To validate the results obtained, two popular algorithms namely, non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are utilized. Since the solution-quality of all meta-heuristic algorithms severely depends on their parameters, Taguchi method has been utilized to fine tune the parameters of all algorithms. The computational results, obtained by implementing the algorithms on several problems of different sizes, demonstrate the reliable performances of the proposed methodology.

Page 1 from 1     

© 2020 All Rights Reserved | International Journal of Industrial Engineering & Production Research

Designed & Developed by : Yektaweb